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Home Team Advantage Calculator

Nearly all sports teams benefit from the phenomena known as home team advantage, but none more than soccer teams. This is surprisingly measurable: a group of mathematicians studied statistics of thousands of soccer matches, and not only proved the prevalence of a home team bias, but also determined why it exists, based on the influence that various accompanying factors exerted.

In software design, a “black box” refers to an arbitrarily created algorithm that spits out answers without communicating to the end user how it arrived at them. The mathematicians had essentially busted open an anthropological black box—one that had been subliminally yet collectively created by fans, stadium architects, and referees, among others. I wanted to expose the weight each of their discovered factors held in the overall “algorithm” that was home team advantage, and communicate it in a way that was accessible to a broader audience than their symbol-filled formulas were.



The papers that I read about home team advantage within professional soccer were very thorough, but they were math-heavy and took a lot of time and energy to understand. If I were to make this information more fun to absorb, I’d first have to understand it myself.

My first visualization approach was in print form. I explored how static, 2-D visualizations could convey the information about home team advantage.

The problem with my print explorations was that they didn’t feel like they were telling a story. The user had to examine the evidence independently; I felt it would help to walk them through the variables involved and showing how they all fit together, so I started sketching ideas for an interactive module. I planned to visualize each variable that affected home team advantage, and allow the user to change the variables’ values to see how that affected the team’s chances of winning.

I sketched out how to visualize things like location, distance, and altitude relative to other teams in a league, and stadium size and constituency of fans, while allowing the user to manipulate the settings for each.

Referee bias is one of the most [compelling] reasons home team advantage exists and this bias is more likely to affect inexperienced refs. So one of the facets I wanted my users to be able to explore and manipulate was referee experience, to see how it affects the home team advantage.

I needed to visually communicate ref experience, so I started by sketching ref’s faces, trying to communicate a spectrum of inexperience to experience, just by showing their faces.

Once I had planned the sequence, layout, and functionality of each facet of the module, I was ready to start iterating on the visual style.

One direction I explored was a very digital looking interface, laid over images relating to the particular facet. I got feedback from my classmates that it felt busy and hard to understand what the images meant, so I kept iterating.


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